Academic Journal
Ensemble genetic and CNN model-based image classification by enhancing hyperparameter tuning
| العنوان: | Ensemble genetic and CNN model-based image classification by enhancing hyperparameter tuning |
|---|---|
| المؤلفون: | Wajahat Hussain, Muhammad Faheem Mushtaq, Mobeen Shahroz, Urooj Akram, Ehab Seif Ghith, Mehdi Tlija, Tai-hoon Kim, Imran Ashraf |
| المصدر: | Scientific Reports, Vol 15, Iss 1, Pp 1-24 (2025) |
| بيانات النشر: | Nature Portfolio, 2025. |
| سنة النشر: | 2025 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Deep learning, Genetic algorithm, Model optimization, Image processing, Optical character recognition, Medicine, Science |
| الوصف: | Abstract Model optimization is a problem of great concern and challenge for developing an image classification model. In image classification, selecting the appropriate hyperparameters can substantially boost the model’s ability to learn intricate patterns and features from complex image data. Hyperparameter optimization helps to prevent overfitting by finding the right balance between complexity and generalization of a model. The ensemble genetic algorithm and convolutional neural network (EGACNN) are proposed to enhance image classification by fine-tuning hyperparameters. The convolutional neural network (CNN) model is combined with a genetic algorithm GA) using stacking based on the Modified National Institute of Standards and Technology (MNIST) dataset to enhance efficiency and prediction rate on image classification. The GA optimizes the number of layers, kernel size, learning rates, dropout rates, and batch sizes of the CNN model to improve the accuracy and performance of the model. The objective of this research is to improve the CNN-based image classification system by utilizing the advantages of ensemble learning and GA. The highest accuracy is obtained using the proposed EGACNN model which is 99.91% and the ensemble CNN and spiking neural network (CSNN) model shows an accuracy of 99.68%. The ensemble approaches like EGACNN and CSNN tends to be more effective as compared to CNN, RNN, AlexNet, ResNet, and VGG models. The hyperparameter optimization of deep learning classification models reduces human efforts and produces better prediction results. Performance comparison with existing approaches also shows the superior performance of the proposed model. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 59540842 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-024-76178-3 |
| URL الوصول: | https://doaj.org/article/1b6219385e1c4be9a5faaf59540842ba |
| رقم الانضمام: | edsdoj.1b6219385e1c4be9a5faaf59540842ba |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20452322 59540842 |
|---|---|
| DOI: | 10.1038/s41598-024-76178-3 |